In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import datetime
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import plot
from plotly import __version__
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
print(__version__)
%matplotlib inline
df=pd.read_csv('Tata-steel.csv')
df.head(10)
5.6.0
Out[1]:
| Date | Open Price | High Price | Low Price | Close Price | WAP | No.of Shares | No. of Trades | Total Turnover (Rs.) | Deliverable Quantity | % Deli. Qty to Traded Qty | Spread High-Low | Spread Close-Open | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 03-Jul-15 | 308.00 | 308.00 | 299.75 | 300.95 | 302.708180 | 499554 | 6430 | 151219082 | 118810.0 | 23.78 | 8.25 | -7.05 |
| 1 | 06-Jul-15 | 294.90 | 299.60 | 292.30 | 298.75 | 295.646338 | 663354 | 9184 | 196118181 | 117663.0 | 17.74 | 7.30 | 3.85 |
| 2 | 07-Jul-15 | 300.75 | 303.25 | 296.00 | 297.45 | 299.477691 | 877207 | 8581 | 262703927 | 211277.0 | 24.09 | 7.25 | -3.30 |
| 3 | 08-Jul-15 | 293.80 | 294.00 | 282.00 | 283.40 | 286.875105 | 1446189 | 20272 | 414875621 | 563505.0 | 38.96 | 12.00 | -10.40 |
| 4 | 09-Jul-15 | 285.20 | 287.25 | 279.60 | 280.55 | 282.860975 | 974983 | 13946 | 275784642 | 330248.0 | 33.87 | 7.65 | -4.65 |
| 5 | 10-Jul-15 | 282.00 | 284.60 | 279.00 | 280.95 | 281.406322 | 702436 | 9268 | 197669931 | 135447.0 | 19.28 | 5.60 | -1.05 |
| 6 | 13-Jul-15 | 281.90 | 285.60 | 279.15 | 284.45 | 282.905447 | 658957 | 8413 | 186422525 | 136197.0 | 20.67 | 6.45 | 2.55 |
| 7 | 14-Jul-15 | 285.00 | 286.90 | 281.05 | 281.70 | 283.275312 | 561904 | 7309 | 159173531 | 140665.0 | 25.03 | 5.85 | -3.30 |
| 8 | 15-Jul-15 | 283.00 | 284.90 | 279.40 | 280.70 | 281.929804 | 564323 | 7346 | 159099473 | 139267.0 | 24.68 | 5.50 | -2.30 |
| 9 | 16-Jul-15 | 285.00 | 286.75 | 280.60 | 281.50 | 282.752620 | 932539 | 10996 | 263677846 | 230537.0 | 24.72 | 6.15 | -3.50 |
In [2]:
# To change date in the accepted format
df['Date']=pd.to_datetime(df.Date)
df.head(10)
Out[2]:
| Date | Open Price | High Price | Low Price | Close Price | WAP | No.of Shares | No. of Trades | Total Turnover (Rs.) | Deliverable Quantity | % Deli. Qty to Traded Qty | Spread High-Low | Spread Close-Open | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015-07-03 | 308.00 | 308.00 | 299.75 | 300.95 | 302.708180 | 499554 | 6430 | 151219082 | 118810.0 | 23.78 | 8.25 | -7.05 |
| 1 | 2015-07-06 | 294.90 | 299.60 | 292.30 | 298.75 | 295.646338 | 663354 | 9184 | 196118181 | 117663.0 | 17.74 | 7.30 | 3.85 |
| 2 | 2015-07-07 | 300.75 | 303.25 | 296.00 | 297.45 | 299.477691 | 877207 | 8581 | 262703927 | 211277.0 | 24.09 | 7.25 | -3.30 |
| 3 | 2015-07-08 | 293.80 | 294.00 | 282.00 | 283.40 | 286.875105 | 1446189 | 20272 | 414875621 | 563505.0 | 38.96 | 12.00 | -10.40 |
| 4 | 2015-07-09 | 285.20 | 287.25 | 279.60 | 280.55 | 282.860975 | 974983 | 13946 | 275784642 | 330248.0 | 33.87 | 7.65 | -4.65 |
| 5 | 2015-07-10 | 282.00 | 284.60 | 279.00 | 280.95 | 281.406322 | 702436 | 9268 | 197669931 | 135447.0 | 19.28 | 5.60 | -1.05 |
| 6 | 2015-07-13 | 281.90 | 285.60 | 279.15 | 284.45 | 282.905447 | 658957 | 8413 | 186422525 | 136197.0 | 20.67 | 6.45 | 2.55 |
| 7 | 2015-07-14 | 285.00 | 286.90 | 281.05 | 281.70 | 283.275312 | 561904 | 7309 | 159173531 | 140665.0 | 25.03 | 5.85 | -3.30 |
| 8 | 2015-07-15 | 283.00 | 284.90 | 279.40 | 280.70 | 281.929804 | 564323 | 7346 | 159099473 | 139267.0 | 24.68 | 5.50 | -2.30 |
| 9 | 2015-07-16 | 285.00 | 286.75 | 280.60 | 281.50 | 282.752620 | 932539 | 10996 | 263677846 | 230537.0 | 24.72 | 6.15 | -3.50 |
In [3]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1485 entries, 0 to 1484 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 1485 non-null datetime64[ns] 1 Open Price 1485 non-null float64 2 High Price 1485 non-null float64 3 Low Price 1485 non-null float64 4 Close Price 1485 non-null float64 5 WAP 1485 non-null float64 6 No.of Shares 1485 non-null int64 7 No. of Trades 1485 non-null int64 8 Total Turnover (Rs.) 1485 non-null int64 9 Deliverable Quantity 1484 non-null float64 10 % Deli. Qty to Traded Qty 1484 non-null float64 11 Spread High-Low 1485 non-null float64 12 Spread Close-Open 1485 non-null float64 dtypes: datetime64[ns](1), float64(9), int64(3) memory usage: 150.9 KB
In [4]:
# To check whether we have any missing Data
sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')
Out[4]:
<AxesSubplot:>
In [37]:
#EDA
sns.set_palette("GnBu_d")
sns.set_style('whitegrid')
sns.jointplot(x='High Price',y='Low Price',data=df,color='blue')
Out[37]:
<seaborn.axisgrid.JointGrid at 0x1d84e1c3ee0>
In [28]:
df['Open Price'].plot.hist()
Out[28]:
<AxesSubplot:ylabel='Frequency'>
In [29]:
plt.style.use('ggplot')
In [39]:
df['Open Price'].plot.hist(alpha=0.5,bins=25,color='blue')
Out[39]:
<AxesSubplot:ylabel='Frequency'>
In [6]:
df[['Open Price','Close Price','High Price','Close Price']].plot(kind='box')
Out[6]:
<AxesSubplot:>
In [7]:
layout=go.Layout(
title='Stock price of Tata Steel',
xaxis=dict(
title='Date'
),
yaxis=dict(
title='Price'
)
)
df1=[{'x':df['Date'],'y':df['Close Price']}]
plot=go.Figure(data=df1,layout=layout,)
iplot(plot)
In [8]:
#PREDICTION BY A REGRESSION MODEL
#Building the regression MOdel
from sklearn.model_selection import train_test_split
#for preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
#For Model Evaluation
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
In [9]:
# Split the data into train and test sets
X=np.array(df.index).reshape(-1,1)
Y=df['Close Price']
X_train, X_test, Y_train,Y_test = train_test_split(X,Y,test_size=0.15,random_state=1001)
In [10]:
#feature Scaling
scaler= StandardScaler().fit(X_train)
from sklearn.linear_model import LinearRegression
In [11]:
#creating a linear model
lm=LinearRegression()
lm.fit(X_train,Y_train)
Out[11]:
LinearRegression()
In [12]:
#Plot Actual And predicted values for train dataset
trace0 = go.Scatter(
x = X_train.T[0],
y=Y_train,
mode='markers',
name='Actual'
)
trace1 = go.Scatter(
x = X_train.T[0],
y=lm.predict(X_train).T,
mode='lines',
name='Predicted'
)
df1=[trace0,trace1]
layout.xaxis.title.text='Day'
plot2 = go.Figure(data=df1,layout=layout)
iplot(plot2)
In [13]:
scores =f'''
{'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}
{'r2_score'.ljust(10)}{r2_score(Y_train,lm.predict(X_train))}\t{r2_score(Y_test,lm.predict(X_test))}
{'MSE'.ljust(10)}{mse(Y_train,lm.predict(X_train))}\t{mse(Y_test,lm.predict(X_test))}
'''
print(scores)
Metric Train Test r2_score 0.2113637853338285 0.28138911005029477 MSE 26976.312078813757 28115.874513353803
In [14]:
df1=df.reset_index()['Close Price']
In [15]:
#ANOTHER LINEAR REGRESSION MODEL(Approach 2)
X=df[['Open Price','High Price','Low Price','No.of Shares']]
y=df['Close Price']
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=10)
X_train.shape
Out[15]:
(1113, 4)
In [16]:
from sklearn.linear_model import LinearRegression
lm=LinearRegression()
lm.fit(X_train,y_train)
Out[16]:
LinearRegression()
In [17]:
# The coefficients
print('Coefficients: \n', lm.coef_)
Coefficients: [-5.91825043e-01 8.63147499e-01 7.26487405e-01 -1.80797315e-07]
In [18]:
predictions = lm.predict(
X_test)
In [35]:
plt.scatter(y_test,predictions,color='blue')
plt.xlabel('Y Test',fontsize=20,color='red')
plt.ylabel('Predicted Y',fontsize=20)
plt.plot
Out[35]:
<function matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)>
In [40]:
# calculate these metrics by hand!
from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(y_test, predictions))
print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))
MAE: 2.8607786203751284 MSE: 18.917985128026437 RMSE: 4.349481018239583
In [41]:
scores =f'''
{'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}
{'r2_score'.ljust(10)}{r2_score(y_train,lm.predict(X_train))}\t{r2_score(y_test,lm.predict(X_test))}
{'MSE'.ljust(10)}{mse(y_train,lm.predict(X_train))}\t{mse(y_test,lm.predict(X_test))}
'''
print(scores)
Metric Train Test r2_score 0.9995360586342691 0.9995019419848405 MSE 15.741870667697818 18.917985128026437
In [42]:
#NOW WE WOLL DO PRDCTION USING TENSORFLOW AND PREDICT THE PRICE FOR NEXT 10 DAYS
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))
df1=scaler.fit_transform(np.array(df1).reshape(-1,1))
In [43]:
print (df1)
[[0.09646022] [0.09432957] [0.09307055] ... [0.93506368] [0.93167401] [0.90513777]]
In [44]:
training_size=int(len(df1)*0.65)
test_size=len(df1)-training_size
train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]
In [45]:
training_size,test_size
Out[45]:
(965, 520)
In [46]:
train_data
Out[46]:
array([[0.09646022],
[0.09432957],
[0.09307055],
[0.07946346],
[0.07670331],
[0.0770907 ],
[0.08048036],
[0.07781705],
[0.07684858],
[0.07762336],
[0.08033509],
[0.08004455],
[0.0709893 ],
[0.07650961],
[0.06677643],
[0.06174035],
[0.04847223],
[0.04648685],
[0.04774587],
[0.04551838],
[0.04450148],
[0.04517941],
[0.05331461],
[0.05898019],
[0.05806014],
[0.0587865 ],
[0.05791487],
[0.04411409],
[0.04624473],
[0.03123335],
[0.03462302],
[0.04377512],
[0.04871435],
[0.04701951],
[0.03902959],
[0.03476829],
[0.00464869],
[0.01239649],
[0.01370394],
[0.02372766],
[0.02629413],
[0.02295288],
[0.01438187],
[0.01772311],
[0.02740787],
[0.01704518],
[0.01409133],
[0.02658467],
[0.03670524],
[0.03772214],
[0.03070069],
[0.03869062],
[0.02682679],
[0.02794054],
[0.02634255],
[0.02469614],
[0.01806208],
[0.0191274 ],
[0.01360709],
[0.00784466],
[0. ],
[0.01021742],
[0.01045954],
[0.02242022],
[0.02498668],
[0.03433248],
[0.03791584],
[0.04784272],
[0.04823011],
[0.04246768],
[0.04517941],
[0.05224929],
[0.04968282],
[0.04532468],
[0.03820638],
[0.04208029],
[0.04329088],
[0.0465837 ],
[0.04319403],
[0.04445305],
[0.04459832],
[0.04396882],
[0.03612416],
[0.03360612],
[0.033364 ],
[0.02348555],
[0.01815893],
[0.0201443 ],
[0.0160767 ],
[0.01888528],
[0.0201443 ],
[0.02755315],
[0.03210498],
[0.02372766],
[0.02668152],
[0.0274563 ],
[0.02232337],
[0.02227495],
[0.02571304],
[0.02890901],
[0.02779526],
[0.03515568],
[0.04082127],
[0.03757687],
[0.0376253 ],
[0.04086969],
[0.0323471 ],
[0.02469614],
[0.03060384],
[0.03830323],
[0.04232241],
[0.0414992 ],
[0.04217713],
[0.05345988],
[0.05229771],
[0.05539683],
[0.05408939],
[0.06038449],
[0.06019079],
[0.05103869],
[0.05210401],
[0.05500944],
[0.05636531],
[0.05418624],
[0.0538957 ],
[0.07045664],
[0.06503317],
[0.04721321],
[0.05069972],
[0.04905331],
[0.04358142],
[0.04421093],
[0.03617258],
[0.02764999],
[0.03379982],
[0.03544623],
[0.02939325],
[0.03215341],
[0.04430778],
[0.050845 ],
[0.05442836],
[0.05137766],
[0.04716479],
[0.04653528],
[0.02919955],
[0.02193598],
[0.02401821],
[0.03162074],
[0.03205656],
[0.03312188],
[0.03321873],
[0.02290446],
[0.01597986],
[0.04343615],
[0.04212871],
[0.04842381],
[0.0476006 ],
[0.05026391],
[0.05215244],
[0.0504576 ],
[0.04503414],
[0.04522783],
[0.0455668 ],
[0.04624473],
[0.05379885],
[0.06372573],
[0.08227204],
[0.08425742],
[0.08764709],
[0.0911336 ],
[0.09181153],
[0.09016513],
[0.09195681],
[0.09534647],
[0.09476539],
[0.09147257],
[0.09747712],
[0.09834875],
[0.10469227],
[0.11161687],
[0.09558859],
[0.09931722],
[0.11917098],
[0.11442545],
[0.11268219],
[0.11510338],
[0.10725873],
[0.12309331],
[0.11873517],
[0.11708876],
[0.1257082 ],
[0.11892887],
[0.12711249],
[0.12943683],
[0.15059803],
[0.14822527],
[0.14764418],
[0.14159121],
[0.15209917],
[0.15035591],
[0.14173648],
[0.14449663],
[0.14086485],
[0.14246283],
[0.12357755],
[0.1265314 ],
[0.12406179],
[0.1236744 ],
[0.12260907],
[0.12362597],
[0.12536923],
[0.11781512],
[0.12105951],
[0.1216406 ],
[0.12352913],
[0.12081739],
[0.11771827],
[0.10871144],
[0.11258535],
[0.11912256],
[0.11975207],
[0.12362597],
[0.11791197],
[0.12890417],
[0.12667667],
[0.13490872],
[0.13297177],
[0.13205172],
[0.13703937],
[0.13573193],
[0.13626459],
[0.12938841],
[0.1185899 ],
[0.11975207],
[0.1244976 ],
[0.12464287],
[0.12023631],
[0.13055058],
[0.12832308],
[0.1265314 ],
[0.12822624],
[0.10764612],
[0.1055639 ],
[0.10662922],
[0.10856617],
[0.11674979],
[0.12202799],
[0.12575662],
[0.1285652 ],
[0.11263377],
[0.11355382],
[0.12193114],
[0.13660355],
[0.15142124],
[0.15287395],
[0.16609365],
[0.1590722 ],
[0.16357561],
[0.16183236],
[0.15587623],
[0.15878166],
[0.15732894],
[0.16105758],
[0.15645731],
[0.14425452],
[0.14856423],
[0.1578616 ],
[0.15548884],
[0.15132439],
[0.16725582],
[0.17388988],
[0.17258244],
[0.16861169],
[0.16347877],
[0.16188078],
[0.16798218],
[0.17224347],
[0.18401046],
[0.17999128],
[0.18493051],
[0.1802334 ],
[0.17829645],
[0.17316353],
[0.16565784],
[0.16318822],
[0.16633577],
[0.17088761],
[0.16376931],
[0.1670137 ],
[0.16691686],
[0.17868384],
[0.18265459],
[0.19519636],
[0.18706116],
[0.16682001],
[0.16696528],
[0.16033122],
[0.15272868],
[0.15514987],
[0.15892693],
[0.16265556],
[0.16943489],
[0.1651736 ],
[0.16372089],
[0.16125127],
[0.17282456],
[0.16125127],
[0.16759479],
[0.17577841],
[0.18279986],
[0.18304198],
[0.18202508],
[0.19858602],
[0.20923926],
[0.20580117],
[0.20391264],
[0.20275047],
[0.21650283],
[0.21703549],
[0.21994092],
[0.21882718],
[0.21776185],
[0.20739916],
[0.19127403],
[0.1895792 ],
[0.1966975 ],
[0.19689119],
[0.2095298 ],
[0.20560748],
[0.19558375],
[0.19534163],
[0.20352525],
[0.20071667],
[0.19219408],
[0.22807612],
[0.21839136],
[0.18614111],
[0.18439785],
[0.18556002],
[0.17795748],
[0.16483463],
[0.1710813 ],
[0.18497894],
[0.19069294],
[0.19984504],
[0.19926396],
[0.20115249],
[0.2068665 ],
[0.201782 ],
[0.19858602],
[0.20551063],
[0.21015931],
[0.20449373],
[0.2229432 ],
[0.2217326 ],
[0.2156312 ],
[0.21238681],
[0.20938453],
[0.2109341 ],
[0.20604329],
[0.20304101],
[0.19616483],
[0.19713331],
[0.18502736],
[0.18342937],
[0.17345407],
[0.18536633],
[0.18105661],
[0.18507578],
[0.18376834],
[0.19815021],
[0.19543848],
[0.19732701],
[0.2109341 ],
[0.21146676],
[0.21374268],
[0.22231369],
[0.23897148],
[0.23863251],
[0.23732507],
[0.24894678],
[0.24487918],
[0.25785676],
[0.25737252],
[0.24526657],
[0.25209433],
[0.25461237],
[0.25916421],
[0.25945475],
[0.25238487],
[0.25282069],
[0.2634255 ],
[0.2664762 ],
[0.26206963],
[0.26269914],
[0.26332865],
[0.26085904],
[0.2503995 ],
[0.25800203],
[0.2626023 ],
[0.26236018],
[0.25059319],
[0.25998741],
[0.25819573],
[0.27635466],
[0.28013171],
[0.27306184],
[0.27480509],
[0.27068907],
[0.27243233],
[0.28952593],
[0.28444143],
[0.28439301],
[0.28240763],
[0.27180282],
[0.26298969],
[0.25969687],
[0.25805046],
[0.26279599],
[0.26846158],
[0.28841218],
[0.29122076],
[0.28608784],
[0.28594257],
[0.27834003],
[0.28482882],
[0.28264975],
[0.26758995],
[0.26976902],
[0.27311026],
[0.26807419],
[0.27243233],
[0.27504721],
[0.28279502],
[0.29083337],
[0.28235921],
[0.28482882],
[0.27790422],
[0.26788049],
[0.25437025],
[0.25248172],
[0.2410537 ],
[0.24221587],
[0.24468549],
[0.24168321],
[0.24173164],
[0.24463706],
[0.24492761],
[0.23669556],
[0.23969784],
[0.23621132],
[0.23175633],
[0.23369328],
[0.22434749],
[0.22337901],
[0.2247833 ],
[0.22909302],
[0.23151421],
[0.22797927],
[0.24638032],
[0.24759092],
[0.28279502],
[0.2764515 ],
[0.27896954],
[0.27625781],
[0.27993802],
[0.27272287],
[0.27499879],
[0.30066341],
[0.2967895 ],
[0.29959808],
[0.29398092],
[0.28720159],
[0.28110019],
[0.2807128 ],
[0.27955063],
[0.28056753],
[0.28821849],
[0.29533679],
[0.29635369],
[0.29756428],
[0.29199555],
[0.29180185],
[0.29126919],
[0.30792698],
[0.31073556],
[0.30632899],
[0.30332672],
[0.29640211],
[0.29935596],
[0.30850806],
[0.32284151],
[0.3321873 ],
[0.33707811],
[0.33412426],
[0.33741707],
[0.34187206],
[0.3413394 ],
[0.34656917],
[0.34448695],
[0.34623021],
[0.35024938],
[0.34685972],
[0.34623021],
[0.35005569],
[0.35799719],
[0.3433732 ],
[0.34017723],
[0.33518958],
[0.34066147],
[0.35257373],
[0.34831243],
[0.33920876],
[0.35465595],
[0.35896567],
[0.35504334],
[0.34690814],
[0.36235533],
[0.386083 ],
[0.4013365 ],
[0.39092538],
[0.39048956],
[0.38235437],
[0.40666312],
[0.41489516],
[0.41111811],
[0.41068229],
[0.40734105],
[0.40477459],
[0.4184301 ],
[0.42380514],
[0.42162607],
[0.41528255],
[0.4235146 ],
[0.42288509],
[0.43440996],
[0.43072975],
[0.4355237 ],
[0.43591109],
[0.43891337],
[0.43983342],
[0.44545058],
[0.46661179],
[0.46327054],
[0.46128517],
[0.46288315],
[0.45683018],
[0.46021984],
[0.47096993],
[0.47019515],
[0.43891337],
[0.42496731],
[0.43852598],
[0.43290882],
[0.43445838],
[0.4355237 ],
[0.44482107],
[0.44293255],
[0.44438526],
[0.47460171],
[0.48017045],
[0.47387536],
[0.46452956],
[0.47406905],
[0.49319646],
[0.49784514],
[0.49498814],
[0.49198586],
[0.4871919 ],
[0.49392281],
[0.50171905],
[0.49949155],
[0.51145223],
[0.50835311],
[0.50113796],
[0.48646555],
[0.48975837],
[0.48874147],
[0.49155005],
[0.49140477],
[0.47571546],
[0.4749891 ],
[0.48612658],
[0.4843349 ],
[0.47406905],
[0.46859716],
[0.46167256],
[0.47000145],
[0.48423805],
[0.48360854],
[0.49382596],
[0.49416493],
[0.49382596],
[0.4904363 ],
[0.48472229],
[0.48704663],
[0.49193744],
[0.4782335 ],
[0.46423902],
[0.47101835],
[0.45963876],
[0.4519878 ],
[0.47121205],
[0.47987991],
[0.48293061],
[0.47247107],
[0.46559489],
[0.46922667],
[0.4780398 ],
[0.48748245],
[0.49290591],
[0.48540022],
[0.49794199],
[0.49319646],
[0.503656 ],
[0.50404339],
[0.51648831],
[0.51435766],
[0.50588349],
[0.51111326],
[0.51518086],
[0.54176553],
[0.55101448],
[0.54970704],
[0.55295143],
[0.55251562],
[0.5522735 ],
[0.55174084],
[0.56350782],
[0.54714057],
[0.55483996],
[0.53314609],
[0.5329524 ],
[0.53338821],
[0.56045712],
[0.54433199],
[0.54975546],
[0.56307201],
[0.55609898],
[0.48782141],
[0.47934725],
[0.45358578],
[0.44859813],
[0.44898552],
[0.44472423],
[0.45518377],
[0.46709602],
[0.49503656],
[0.49101738],
[0.48278534],
[0.47159944],
[0.43281197],
[0.43523316],
[0.4253547 ],
[0.42273982],
[0.46143044],
[0.46380321],
[0.45566801],
[0.45552273],
[0.45900925],
[0.43968815],
[0.4446758 ],
[0.43256985],
[0.42017336],
[0.39150646],
[0.40806741],
[0.41349087],
[0.40588833],
[0.39712363],
[0.38656724],
[0.36191952],
[0.37959421],
[0.3689894 ],
[0.36724614],
[0.3537359 ],
[0.36908624],
[0.37644666],
[0.35790034],
[0.36530919],
[0.36622924],
[0.34777977],
[0.36768195],
[0.3728633 ],
[0.37305699],
[0.38772941],
[0.39354026],
[0.38467871],
[0.38153116],
[0.37872258],
[0.38206382],
[0.38753571],
[0.40598518],
[0.39223282],
[0.39160331],
[0.38438817],
[0.37271803],
[0.36579342],
[0.37605927],
[0.38095008],
[0.36191952],
[0.37218537],
[0.3679725 ],
[0.38216067],
[0.38235437],
[0.38845576],
[0.38003002],
[0.39252336],
[0.39586461],
[0.40937485],
[0.4074379 ],
[0.39581618],
[0.37785095],
[0.3637112 ],
[0.36400174],
[0.32724808],
[0.33610963],
[0.35431698],
[0.36245218],
[0.36211321],
[0.36487337],
[0.36230691],
[0.34768292],
[0.35407486],
[0.35891724],
[0.36642293],
[0.38739044],
[0.38559876],
[0.37518764],
[0.36651978],
[0.35460753],
[0.35644763],
[0.35310639],
[0.34608494],
[0.3423563 ],
[0.35053993],
[0.3456007 ],
[0.34777977],
[0.34589124],
[0.33702968],
[0.33112198],
[0.33577066],
[0.35494649],
[0.34826401],
[0.35363905],
[0.35596339],
[0.34104886],
[0.34167837],
[0.34429325],
[0.35567285],
[0.34332478],
[0.34376059],
[0.34555227],
[0.30792698],
[0.32071086],
[0.29378723],
[0.28899327],
[0.29257663],
[0.30555421],
[0.3150937 ],
[0.32269624],
[0.32308363],
[0.33712653],
[0.34017723],
[0.35010411],
[0.34216261],
[0.34201734],
[0.34259842],
[0.34095201],
[0.36032153],
[0.36443756],
[0.3716527 ],
[0.36240376],
[0.35625393],
[0.36564815],
[0.35518861],
[0.36729456],
[0.38550191],
[0.36889255],
[0.35848143],
[0.36230691],
[0.36773038],
[0.37576873],
[0.38032057],
[0.39412135],
[0.38768098],
[0.38656724],
[0.37935209],
[0.38017529],
[0.38651881],
[0.40448404],
[0.39915743],
[0.37862573],
[0.39140962],
[0.40094911],
[0.40123965],
[0.39814053],
[0.40588833],
[0.40985909],
[0.39247494],
[0.38603457],
[0.39484771],
[0.39567091],
[0.36705244],
[0.37010314],
[0.36642293],
[0.36855358],
[0.3565929 ],
[0.34715026],
[0.35925621],
[0.36966733],
[0.34371217],
[0.36182267],
[0.35799719],
[0.36099947],
[0.34216261],
[0.34114571],
[0.33276839],
[0.33388214],
[0.3374655 ],
[0.33431795],
[0.33872452],
[0.36051523],
[0.35228318],
[0.33998354],
[0.34409956],
[0.3606605 ],
[0.36497022],
[0.36380805],
[0.36714929],
[0.36022469],
[0.36952206],
[0.37557503],
[0.37538134],
[0.37509079],
[0.36099947],
[0.36884412],
[0.35073362],
[0.34012881],
[0.32792601],
[0.31935499],
[0.31063871],
[0.31247881],
[0.32163091],
[0.31853179],
[0.3252627 ],
[0.32632802],
[0.30405307],
[0.29993705],
[0.29688635],
[0.29122076],
[0.29078495],
[0.3089923 ],
[0.30109922],
[0.30221297],
[0.31155876],
[0.31538424],
[0.31519055],
[0.31194615],
[0.30870176],
[0.30318144],
[0.30245509],
[0.2967895 ],
[0.30250351],
[0.31039659],
[0.30429519],
[0.28327926],
[0.27088277],
[0.27732313],
[0.2756283 ],
[0.28085807],
[0.26923636],
[0.27015641],
[0.26473294],
[0.26085904],
[0.26516876],
[0.26449082],
[0.2626023 ],
[0.26066534],
[0.26168224],
[0.24739722],
[0.25417655],
[0.25127112],
[0.24337804],
[0.23659871],
[0.23572708],
[0.25785676],
[0.26594354],
[0.26347392],
[0.26579827],
[0.25509661],
[0.27412716],
[0.27722628],
[0.25974529],
[0.27025326],
[0.27756525],
[0.27315868],
[0.27228706],
[0.25771149],
[0.25606508],
[0.26129485],
[0.28013171],
[0.28720159],
[0.29112392],
[0.2939325 ],
[0.29325456],
[0.29490097],
[0.29049441],
[0.29615999],
[0.31005762],
[0.30744274],
[0.30928284],
[0.29703162],
[0.30937969],
[0.31262409],
[0.30240666],
[0.30647426],
[0.30429519],
[0.31310832],
[0.31465789],
[0.30235824],
[0.30681323],
[0.30439204],
[0.3079754 ],
[0.30463416],
[0.29601472],
[0.30942811],
[0.32284151],
[0.31935499],
[0.32351944],
[0.31882233],
[0.33606121],
[0.33673914],
[0.33940245],
[0.33204203],
[0.32521428],
[0.32192146],
[0.33959615],
[0.34022565],
[0.33073459],
[0.32342259],
[0.31228512],
[0.31373783],
[0.29979178],
[0.33281681],
[0.34390586],
[0.3423563 ],
[0.33533485],
[0.32366471],
[0.31891918],
[0.31199458],
[0.30758801],
[0.27693574],
[0.26173067],
[0.25993899],
[0.25359547],
[0.25945475],
[0.25974529],
[0.27291657],
[0.26357077],
[0.26376447],
[0.25621035],
[0.27325553],
[0.30032444],
[0.29630526],
[0.2827466 ]])
In [47]:
import numpy
# convert an array of values into a dataset matrix
def create_dataset(dataset, time_step=1):
dataX, dataY = [], []
for i in range(len(dataset)-time_step-1):
a = dataset[i:(i+time_step), 0] ###i=0, 0,1,2,3-----99 100
dataX.append(a)
dataY.append(dataset[i + time_step, 0])
return numpy.array(dataX), numpy.array(dataY)
In [48]:
# reshape into X=t,t+1,t+2,t+3 and Y=t+4
time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, ytest = create_dataset(test_data, time_step)
In [49]:
print(X_test.shape), print(ytest.shape)
(419, 100) (419,)
Out[49]:
(None, None)
In [50]:
# reshape input to be [samples, time steps, features] which is required for LSTM
X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)
X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)
In [51]:
### Create the Stacked LSTM model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
In [52]:
model=Sequential()
model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))
model.add(LSTM(50,return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error',optimizer='adam')
In [58]:
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 100, 50) 10400
lstm_1 (LSTM) (None, 100, 50) 20200
lstm_2 (LSTM) (None, 50) 20200
dense (Dense) (None, 1) 51
=================================================================
Total params: 50,851
Trainable params: 50,851
Non-trainable params: 0
_________________________________________________________________
In [59]:
model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)
Epoch 1/100 14/14 [==============================] - 7s 189ms/step - loss: 0.0216 - val_loss: 0.0306 Epoch 2/100 14/14 [==============================] - 2s 130ms/step - loss: 0.0039 - val_loss: 0.0121 Epoch 3/100 14/14 [==============================] - 2s 131ms/step - loss: 0.0017 - val_loss: 0.0052 Epoch 4/100 14/14 [==============================] - 2s 132ms/step - loss: 7.9742e-04 - val_loss: 0.0036 Epoch 5/100 14/14 [==============================] - 2s 117ms/step - loss: 7.2694e-04 - val_loss: 0.0039 Epoch 6/100 14/14 [==============================] - 2s 122ms/step - loss: 7.0373e-04 - val_loss: 0.0043 Epoch 7/100 14/14 [==============================] - 2s 121ms/step - loss: 6.7829e-04 - val_loss: 0.0043 Epoch 8/100 14/14 [==============================] - 2s 116ms/step - loss: 6.9737e-04 - val_loss: 0.0050 Epoch 9/100 14/14 [==============================] - 2s 127ms/step - loss: 6.7471e-04 - val_loss: 0.0042 Epoch 10/100 14/14 [==============================] - 2s 122ms/step - loss: 6.6030e-04 - val_loss: 0.0039 Epoch 11/100 14/14 [==============================] - 2s 132ms/step - loss: 6.3781e-04 - val_loss: 0.0042 Epoch 12/100 14/14 [==============================] - 2s 163ms/step - loss: 6.2818e-04 - val_loss: 0.0037 Epoch 13/100 14/14 [==============================] - 2s 142ms/step - loss: 6.1883e-04 - val_loss: 0.0034 Epoch 14/100 14/14 [==============================] - 2s 125ms/step - loss: 6.1132e-04 - val_loss: 0.0036 Epoch 15/100 14/14 [==============================] - 2s 120ms/step - loss: 5.9963e-04 - val_loss: 0.0034 Epoch 16/100 14/14 [==============================] - 2s 116ms/step - loss: 6.0136e-04 - val_loss: 0.0037 Epoch 17/100 14/14 [==============================] - 2s 117ms/step - loss: 6.3481e-04 - val_loss: 0.0032 Epoch 18/100 14/14 [==============================] - 2s 117ms/step - loss: 5.7008e-04 - val_loss: 0.0031 Epoch 19/100 14/14 [==============================] - 2s 117ms/step - loss: 5.7846e-04 - val_loss: 0.0033 Epoch 20/100 14/14 [==============================] - 2s 115ms/step - loss: 5.8275e-04 - val_loss: 0.0029 Epoch 21/100 14/14 [==============================] - 2s 138ms/step - loss: 5.2693e-04 - val_loss: 0.0025 Epoch 22/100 14/14 [==============================] - 2s 138ms/step - loss: 5.1990e-04 - val_loss: 0.0028 Epoch 23/100 14/14 [==============================] - 2s 155ms/step - loss: 5.0517e-04 - val_loss: 0.0026 Epoch 24/100 14/14 [==============================] - 2s 131ms/step - loss: 5.0354e-04 - val_loss: 0.0029 Epoch 25/100 14/14 [==============================] - 2s 125ms/step - loss: 5.0409e-04 - val_loss: 0.0023 Epoch 26/100 14/14 [==============================] - 2s 139ms/step - loss: 4.9685e-04 - val_loss: 0.0027 Epoch 27/100 14/14 [==============================] - 2s 132ms/step - loss: 5.3307e-04 - val_loss: 0.0024 Epoch 28/100 14/14 [==============================] - 2s 134ms/step - loss: 4.8449e-04 - val_loss: 0.0019 Epoch 29/100 14/14 [==============================] - 2s 125ms/step - loss: 4.7568e-04 - val_loss: 0.0020 Epoch 30/100 14/14 [==============================] - 2s 141ms/step - loss: 4.7949e-04 - val_loss: 0.0022 Epoch 31/100 14/14 [==============================] - 2s 133ms/step - loss: 4.5560e-04 - val_loss: 0.0017 Epoch 32/100 14/14 [==============================] - 2s 119ms/step - loss: 4.7166e-04 - val_loss: 0.0015 Epoch 33/100 14/14 [==============================] - 2s 118ms/step - loss: 4.8998e-04 - val_loss: 0.0016 Epoch 34/100 14/14 [==============================] - 2s 118ms/step - loss: 4.5278e-04 - val_loss: 0.0029 Epoch 35/100 14/14 [==============================] - 2s 134ms/step - loss: 4.6314e-04 - val_loss: 0.0027 Epoch 36/100 14/14 [==============================] - 2s 140ms/step - loss: 4.9327e-04 - val_loss: 0.0021 Epoch 37/100 14/14 [==============================] - 2s 117ms/step - loss: 4.3201e-04 - val_loss: 0.0018 Epoch 38/100 14/14 [==============================] - 2s 150ms/step - loss: 3.9871e-04 - val_loss: 0.0020 Epoch 39/100 14/14 [==============================] - 2s 137ms/step - loss: 4.1456e-04 - val_loss: 0.0020 Epoch 40/100 14/14 [==============================] - 2s 126ms/step - loss: 4.5849e-04 - val_loss: 0.0021 Epoch 41/100 14/14 [==============================] - 2s 116ms/step - loss: 4.3615e-04 - val_loss: 0.0017 Epoch 42/100 14/14 [==============================] - 2s 116ms/step - loss: 3.8359e-04 - val_loss: 0.0015 Epoch 43/100 14/14 [==============================] - 2s 116ms/step - loss: 3.5947e-04 - val_loss: 0.0015 Epoch 44/100 14/14 [==============================] - 2s 119ms/step - loss: 3.5385e-04 - val_loss: 0.0016 Epoch 45/100 14/14 [==============================] - 2s 118ms/step - loss: 3.4993e-04 - val_loss: 0.0016 Epoch 46/100 14/14 [==============================] - 2s 120ms/step - loss: 3.6205e-04 - val_loss: 0.0014 Epoch 47/100 14/14 [==============================] - 2s 117ms/step - loss: 3.2851e-04 - val_loss: 0.0013 Epoch 48/100 14/14 [==============================] - 2s 129ms/step - loss: 3.2961e-04 - val_loss: 0.0015 Epoch 49/100 14/14 [==============================] - 2s 138ms/step - loss: 3.2473e-04 - val_loss: 0.0012 Epoch 50/100 14/14 [==============================] - 2s 122ms/step - loss: 3.2092e-04 - val_loss: 0.0014 Epoch 51/100 14/14 [==============================] - 2s 132ms/step - loss: 3.1836e-04 - val_loss: 0.0016 Epoch 52/100 14/14 [==============================] - 2s 133ms/step - loss: 3.1662e-04 - val_loss: 9.9691e-04 Epoch 53/100 14/14 [==============================] - 2s 135ms/step - loss: 3.5311e-04 - val_loss: 0.0011 Epoch 54/100 14/14 [==============================] - 2s 128ms/step - loss: 3.0084e-04 - val_loss: 0.0011 Epoch 55/100 14/14 [==============================] - 2s 126ms/step - loss: 2.9835e-04 - val_loss: 0.0014 Epoch 56/100 14/14 [==============================] - 2s 125ms/step - loss: 3.0461e-04 - val_loss: 0.0012 Epoch 57/100 14/14 [==============================] - 2s 135ms/step - loss: 3.0464e-04 - val_loss: 9.2226e-04 Epoch 58/100 14/14 [==============================] - 2s 138ms/step - loss: 3.3423e-04 - val_loss: 0.0012 Epoch 59/100 14/14 [==============================] - 2s 130ms/step - loss: 2.9653e-04 - val_loss: 0.0014 Epoch 60/100 14/14 [==============================] - 2s 124ms/step - loss: 3.2118e-04 - val_loss: 0.0013 Epoch 61/100 14/14 [==============================] - 2s 129ms/step - loss: 2.8639e-04 - val_loss: 0.0012 Epoch 62/100 14/14 [==============================] - 2s 118ms/step - loss: 2.7868e-04 - val_loss: 0.0010 Epoch 63/100 14/14 [==============================] - 2s 133ms/step - loss: 3.0204e-04 - val_loss: 0.0012 Epoch 64/100 14/14 [==============================] - 2s 143ms/step - loss: 3.1422e-04 - val_loss: 0.0015 Epoch 65/100 14/14 [==============================] - 2s 128ms/step - loss: 3.4381e-04 - val_loss: 0.0013 Epoch 66/100 14/14 [==============================] - 2s 154ms/step - loss: 2.7934e-04 - val_loss: 8.5070e-04 Epoch 67/100 14/14 [==============================] - 2s 142ms/step - loss: 2.8754e-04 - val_loss: 9.2565e-04 Epoch 68/100 14/14 [==============================] - 2s 125ms/step - loss: 2.7482e-04 - val_loss: 0.0012 Epoch 69/100 14/14 [==============================] - 2s 137ms/step - loss: 2.8165e-04 - val_loss: 0.0012 Epoch 70/100 14/14 [==============================] - 2s 130ms/step - loss: 2.8345e-04 - val_loss: 0.0016 Epoch 71/100 14/14 [==============================] - 2s 126ms/step - loss: 2.9024e-04 - val_loss: 7.6359e-04 Epoch 72/100 14/14 [==============================] - 2s 126ms/step - loss: 2.6435e-04 - val_loss: 9.8066e-04 Epoch 73/100 14/14 [==============================] - 2s 127ms/step - loss: 2.5267e-04 - val_loss: 7.1246e-04 Epoch 74/100 14/14 [==============================] - 2s 144ms/step - loss: 2.8395e-04 - val_loss: 7.7438e-04 Epoch 75/100 14/14 [==============================] - 2s 137ms/step - loss: 2.6123e-04 - val_loss: 8.8154e-04 Epoch 76/100 14/14 [==============================] - 2s 130ms/step - loss: 2.4242e-04 - val_loss: 8.9714e-04 Epoch 77/100 14/14 [==============================] - 2s 119ms/step - loss: 2.4386e-04 - val_loss: 0.0011 Epoch 78/100 14/14 [==============================] - 2s 119ms/step - loss: 2.4902e-04 - val_loss: 8.1598e-04 Epoch 79/100 14/14 [==============================] - 2s 119ms/step - loss: 2.4013e-04 - val_loss: 6.7061e-04 Epoch 80/100 14/14 [==============================] - 2s 119ms/step - loss: 3.1375e-04 - val_loss: 0.0012 Epoch 81/100 14/14 [==============================] - 2s 120ms/step - loss: 3.0737e-04 - val_loss: 0.0014 Epoch 82/100 14/14 [==============================] - 2s 119ms/step - loss: 2.5192e-04 - val_loss: 0.0011 Epoch 83/100 14/14 [==============================] - 2s 137ms/step - loss: 2.4572e-04 - val_loss: 0.0010 Epoch 84/100 14/14 [==============================] - 2s 155ms/step - loss: 2.5816e-04 - val_loss: 7.1704e-04 Epoch 85/100 14/14 [==============================] - 2s 138ms/step - loss: 2.3946e-04 - val_loss: 9.8093e-04 Epoch 86/100 14/14 [==============================] - 2s 133ms/step - loss: 2.3071e-04 - val_loss: 6.9482e-04 Epoch 87/100 14/14 [==============================] - 2s 129ms/step - loss: 2.5062e-04 - val_loss: 6.5913e-04 Epoch 88/100 14/14 [==============================] - 2s 123ms/step - loss: 2.4183e-04 - val_loss: 6.9999e-04 Epoch 89/100 14/14 [==============================] - 2s 121ms/step - loss: 2.4447e-04 - val_loss: 0.0013 Epoch 90/100 14/14 [==============================] - 2s 121ms/step - loss: 2.3850e-04 - val_loss: 7.0406e-04 Epoch 91/100 14/14 [==============================] - 2s 124ms/step - loss: 2.4112e-04 - val_loss: 0.0011 Epoch 92/100 14/14 [==============================] - 2s 133ms/step - loss: 2.3137e-04 - val_loss: 7.6536e-04 Epoch 93/100 14/14 [==============================] - 2s 135ms/step - loss: 2.6959e-04 - val_loss: 6.2377e-04 Epoch 94/100 14/14 [==============================] - 2s 140ms/step - loss: 2.3121e-04 - val_loss: 5.7497e-04 Epoch 95/100 14/14 [==============================] - 2s 128ms/step - loss: 2.3352e-04 - val_loss: 7.8791e-04 Epoch 96/100 14/14 [==============================] - 2s 128ms/step - loss: 2.1362e-04 - val_loss: 7.8944e-04 Epoch 97/100 14/14 [==============================] - 2s 121ms/step - loss: 2.1115e-04 - val_loss: 0.0010 Epoch 98/100 14/14 [==============================] - 2s 122ms/step - loss: 2.2986e-04 - val_loss: 5.8679e-04 Epoch 99/100 14/14 [==============================] - 2s 122ms/step - loss: 2.0538e-04 - val_loss: 7.3830e-04 Epoch 100/100 14/14 [==============================] - 2s 132ms/step - loss: 2.0567e-04 - val_loss: 6.0433e-04
Out[59]:
<keras.callbacks.History at 0x1d857546d90>
In [60]:
import tensorflow as tf
In [61]:
tf.__version__
Out[61]:
'2.12.0'
In [62]:
### Lets Do the prediction and check performance metrics
train_predict=model.predict(X_train)
test_predict=model.predict(X_test)
27/27 [==============================] - 2s 29ms/step 14/14 [==============================] - 1s 27ms/step
In [63]:
##Transformback to original form
train_predict=scaler.inverse_transform(train_predict)
test_predict=scaler.inverse_transform(test_predict)
In [64]:
### Calculate RMSE performance metrics
import math
from sklearn.metrics import mean_squared_error
math.sqrt(mean_squared_error(y_train,train_predict))
Out[64]:
0.014140821522976035
In [69]:
### Test Data RMSE
math.sqrt(mean_squared_error(ytest,test_predict))
Out[69]:
0.02458303470611413
In [105]:
### Plotting
# shift train predictions for plotting
look_back=100
trainPredictPlot = numpy.empty_like(df1)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(df1)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(df1),color='red')
plt.plot(trainPredictPlot,color='blue')
plt.plot(testPredictPlot,color='yellow')
plt.xlabel('Days',fontsize=18)
plt.ylabel('Price In rupees',fontsize=20)
plt.legend(["Original Price","train price","test price"],loc="upper left")
Out[105]:
<matplotlib.legend.Legend at 0x1d8670c30a0>
In [76]:
len(test_data)
Out[76]:
520
In [77]:
x_input=test_data[420:].reshape(1,-1)
x_input.shape
Out[77]:
(1, 100)
In [78]:
temp_input=list(x_input)
temp_input=temp_input[0].tolist()
In [79]:
temp_input
Out[79]:
[0.46854873855987594, 0.4857391893855018, 0.48249479444094706, 0.473487966684422, 0.4776039901215437, 0.4639969008764709, 0.4556680063919422, 0.48157474214323753, 0.4797346375478185, 0.48084838506609845, 0.45450583506851955, 0.4670476006004551, 0.5115006537213693, 0.5116459251367971, 0.5244782334995883, 0.49726405500944254, 0.5121785869933659, 0.5170209675076267, 0.5572127257759913, 0.5391990702629412, 0.5151808629122074, 0.5191031911287589, 0.48932255096605487, 0.5063677303762528, 0.5021548593288461, 0.5178441721950509, 0.5062224589608251, 0.4871919035397801, 0.48772456539634884, 0.5148903200813519, 0.5250593191612996, 0.5225897050990267, 0.48622342743692787, 0.505108711442545, 0.5474795409423272, 0.5795360999467336, 0.5913515084015302, 0.6406469420367051, 0.6450535083046824, 0.6406469420367051, 0.6521718076606458, 0.6937194324730036, 0.6758510483753812, 0.6295094668539052, 0.656239407292625, 0.6745436056365308, 0.6665536777880006, 0.6639872161154423, 0.6711055154714056, 0.6971575226381288, 0.7010314270495375, 0.7157522638128904, 0.7513437605927071, 0.7454360563653091, 0.8040772843930073, 0.80635320323471, 0.8359885719819862, 0.8355043339305601, 0.8402498668345357, 0.870853711684664, 0.9500750568979708, 0.9831969396155149, 1.0, 0.9464916953174181, 0.9014091327296496, 0.9193743644375574, 0.9473149000048422, 0.9325940632414893, 0.8750181589269284, 0.8824270011137474, 0.8624279695898502, 0.8750665827320712, 0.8527432085613285, 0.8670282310783979, 0.8739528352137911, 0.8951624618662535, 0.870611592658951, 0.8931286620502636, 0.8913369812599874, 0.8895937242748535, 0.8977289235388115, 0.8804900489080432, 0.8711926783206625, 0.8844123771245942, 0.9275095637015154, 0.9331267250980579, 0.9424240956854388, 0.911142317563314, 0.8738559876035059, 0.8618953077332816, 0.8756476683937822, 0.8834439010217421, 0.870127354607525, 0.8831533581908866, 0.9332719965134859, 0.9517214662728195, 0.9404871434797346, 0.9350636773037624, 0.9316740109437798, 0.9051377657256305]
In [80]:
# demonstrate prediction for next 10 days
from numpy import array
lst_output=[]
n_steps=100
i=0
while(i<30):
if(len(temp_input)>100):
#print(temp_input)
x_input=np.array(temp_input[1:])
print("{} day input {}".format(i,x_input))
x_input=x_input.reshape(1,-1)
x_input = x_input.reshape((1, n_steps, 1))
#print(x_input)
yhat = model.predict(x_input, verbose=0)
print("{} day output {}".format(i,yhat))
temp_input.extend(yhat[0].tolist())
temp_input=temp_input[1:]
#print(temp_input)
lst_output.extend(yhat.tolist())
i=i+1
else:
x_input = x_input.reshape((1, n_steps,1))
yhat = model.predict(x_input, verbose=0)
print(yhat[0])
temp_input.extend(yhat[0].tolist())
print(len(temp_input))
lst_output.extend(yhat.tolist())
i=i+1
print(lst_output)
[0.8919715] 101 1 day input [0.48573919 0.48249479 0.47348797 0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153] 1 day output [[0.87675655]] 2 day input [0.48249479 0.47348797 0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655] 2 day output [[0.86094797]] 3 day input [0.47348797 0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797] 3 day output [[0.84559965]] 4 day input [0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965] 4 day output [[0.8310815]] 5 day input [0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151] 5 day output [[0.8175638]] 6 day input [0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377] 6 day output [[0.8051158]] 7 day input [0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582] 7 day output [[0.7937276]] 8 day input [0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758] 8 day output [[0.78332597]] 9 day input [0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597] 9 day output [[0.77379394]] 10 day input [0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394] 10 day output [[0.76499057]] 11 day input [0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057] 11 day output [[0.7567651]] 12 day input [0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513] 12 day output [[0.7489709]] 13 day input [0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093] 13 day output [[0.74147487]] 14 day input [0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487] 14 day output [[0.73416233]] 15 day input [0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233] 15 day output [[0.72694296]] 16 day input [0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296] 16 day output [[0.7197505]] 17 day input [0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052] 17 day output [[0.71254283]] 18 day input [0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283] 18 day output [[0.7053]] 19 day input [0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997] 19 day output [[0.698021]] 20 day input [0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099] 20 day output [[0.69071895]] 21 day input [0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895] 21 day output [[0.68341875]] 22 day input [0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875] 22 day output [[0.67615265]] 23 day input [0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265] 23 day output [[0.66895497]] 24 day input [0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497] 24 day output [[0.66186106]] 25 day input [0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106] 25 day output [[0.65490395]] 26 day input [0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395] 26 day output [[0.6481117]] 27 day input [0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395 0.6481117 ] 27 day output [[0.6415083]] 28 day input [0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395 0.6481117 0.64150828] 28 day output [[0.6351101]] 29 day input [0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395 0.6481117 0.64150828 0.63511008] 29 day output [[0.6289286]] [[0.8919715285301208], [0.8767565488815308], [0.8609479665756226], [0.8455996513366699], [0.8310815095901489], [0.8175637722015381], [0.805115818977356], [0.7937275767326355], [0.7833259701728821], [0.7737939357757568], [0.7649905681610107], [0.7567651271820068], [0.7489709258079529], [0.7414748668670654], [0.7341623306274414], [0.7269429564476013], [0.7197505235671997], [0.7125428318977356], [0.705299973487854], [0.6980209946632385], [0.690718948841095], [0.6834187507629395], [0.6761526465415955], [0.6689549684524536], [0.6618610620498657], [0.6549039483070374], [0.6481117010116577], [0.6415082812309265], [0.635110080242157], [0.6289286017417908]]
In [89]:
day_new=np.arange(1,101)
day_pred=np.arange(101,131)
In [90]:
len(df1)
Out[90]:
1485
In [99]:
plt.plot(day_new,scaler.inverse_transform(df1[1385:]),color='red')
plt.plot(day_pred,scaler.inverse_transform(lst_output),color='blue')
plt.xlabel('Days',fontsize=20,color='black')
plt.ylabel('Price In rupees',fontsize=20,color='black')
plt.legend(["Original Price","predicted price"],loc="lower right") #predicted rice for next 10 days
Out[99]:
<matplotlib.legend.Legend at 0x1d866e378b0>
In [96]:
df3=df1.tolist()
df3.extend(lst_output)
plt.plot(df3[1400:],color='black')
Out[96]:
[<matplotlib.lines.Line2D at 0x1d866cebeb0>]
In [123]:
df3=scaler.inverse_transform(df3).tolist()
In [100]:
plt.plot(df3,color='blue')
plt.xlabel('Days',fontsize=20)
plt.ylabel('Price In rupees',fontsize=20)
Out[100]:
Text(0, 0.5, 'Price In rupees')
In [ ]: